AI & Intelligent Agents
We weave AI into your product — autonomous agents, fine-tuned LLMs and retrieval pipelines grounded in your data and business logic.
RAG pipelines over your private knowledge base
Custom agents and multi-step tool orchestration
LLM fine-tuning and prompt engineering
Evaluation harnesses and regression testing for AI features
Structured output generation with validation
Safe, observable model integrations with fallbacks
SaaS products adding intelligent automation to repetitive document workflows
Companies with large private knowledge bases needing accurate Q&A systems
Products where AI-assisted decisions require auditability and rollback
Do you build custom models or use existing ones?
Both. For most products, fine-tuning or prompt-engineering an existing model (GPT-4, Claude, Gemini, Mistral) is faster and cheaper than training from scratch. We recommend the right approach after understanding your data and latency requirements.
How do you prevent hallucinations in production?
Through a combination of RAG (grounding responses in your actual data), evaluation harnesses that catch regressions, structured outputs with Zod/function calling, and human-review gates for high-stakes outputs.
Our data is sensitive. How do you handle security?
Data stays in your infrastructure. We design pipelines with on-premise or private-cloud LLMs where needed, implement row-level access controls, and never log prompts containing PII without explicit consent.
What does an AI integration project look like?
Discovery → data audit → prototype (1–2 weeks) → evaluation harness → production integration → monitoring. We do not ship AI features without an evaluation and rollback plan.